code: def test_produces_correct_output(
      dataset: str,
      task: int,
      metrics: Sequence[Scorer],
      mock_logger: PicklableClientLogger,
      make_automl: Callable[..., AutoML],
      make_sklearn_dataset: Callable[..., XYDataManager],
  ) -> None:
      """
      Parameters
      ----------
      dataset: str
          The name of the dataset
  
      task : int
          The task type of the dataset
  
      metrics: Sequence[Scorer]
          Metric(s) to use, required as fit usually determines the metric to use
  
      Fixtures
      --------
      mock_logger: PickleableClientLogger
          A mock logger to use
  
      make_automl : Callable[..., AutoML]
          Factory to make an AutoML object
  
      make_sklearn_dataset : Callable[..., XYDataManager]
          Factory to get an sklearn dataset
  
      Expects
      -------
      * There should only be one output created with one dummy predictions
      * It should be named "1337_1_0.0" with {seed}_{num_run}_{budget}
      * It should produce predictions "predictions_ensemble_1337_1_0.0.npy"
      """
      seed = 1337
      automl = make_automl(metrics=metrics, seed=seed)
      automl._logger = mock_logger
  
      datamanager = make_sklearn_dataset(
          dataset,
          as_datamanager=True,
          task=task,
          feat_type="numerical",
      )
      automl._backend.save_datamanager(datamanager)
      automl._do_dummy_prediction()
  
      path = Path(automl._backend.get_runs_directory())
      run_paths = list(path.iterdir())
      assert len(run_paths) == 1
  
      dummy_run_path = run_paths[0]
      assert dummy_run_path.name == f"{seed}_1_0.0"
  
      predictions_path = (
          dummy_run_path / f"predictions_ensemble_{seed}_1_0.0.npy"
      )
      assert predictions_path.exists()
  
file_path: auto-sklearn/test/test_automl/test_dummy_predictions.py
imports: from __future__ import annotations
  from typing import Callable, Sequence, Tuple
  from pathlib import Path
  import numpy
  from smac.tae import StatusType
  from autosklearn.automl import AutoML
  from autosklearn.constants import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, REGRESSION
  from autosklearn.data.xy_data_manager import XYDataManager
  from autosklearn.metrics import Scorer, accuracy, log_loss, precision, r2
  from autosklearn.util.logging_ import PicklableClientLogger
  import pytest
  from pytest_cases import parametrize
  from unittest.mock import patch
label_desc: Use image node_id 1 for calling a global function with example usage: test_produces_correct_output(dataset, task, metrics, mock_logger, make_automl, make_sklearn_dataset) without return types